Debt Maturity Structure and Credit Quality∗
Radhakrishnan Gopalan † Fenghua Song ‡ Vijay Yerramilli §
July 2011
Abstract
We examine whether a ﬁrm’s debt maturity structure aﬀects its credit quality. We ﬁnd
that ﬁrms with a larger proportion of their debt maturing within the year (short-term
debt) are more likely to experience a severe fall in their credit quality in the following
year, as measured by the severity of credit rating downgrades and the propensity to
default. This eﬀect is stronger for ﬁrms with declining proﬁtability and during recession
years. Our results are robust to instrumenting for the proportion of short-term debt
and alternate measures of a ﬁrm’s exposure to rollover risk. We also ﬁnd that long-
term bonds issued by ﬁrms with a larger proportion of short-term debt trade at higher
yield spreads, ceteris paribus, which indicates that bond market investors are cognizant
of rollover risk. Overall, our results are broadly consistent with theories which argue
that short-term debt exposes a ﬁrm to rollover risk, thereby increasing the ﬁrm’s overall
credit risk.
∗
An earlier version of the paper was circulated under the title “Do Credit Rating Agencies Underestimate
Liquidity Risk?” We thank Bruce Arnold, Long Chen, Mark Flannery, Paolo Fulghieri, Amar Gande, Murali
u
Jagannathan, Vik Nanda, Lars Norden, David Skeie, Chester Spatt, G¨nter Strobl, Wei Xiong, and seminar
participants at Australia National University, Binghamton, Georgia Tech, HKUST, NUS, UNSW, University
of Sydney, University of Technology Sydney, Washington University in St. Louis, European Finance Asso-
ciation annual conference (2010), Financial Management Association annual conference (2010), FDIC/JFSR
joint conference on ﬁnance and sustainable growth (2010), Texas Lone Star ﬁnance conference (2010), CARE-
FIN/Bocconi conference on matching stability and performance (2010), NY Fed/RCFS joint conference on
ﬁnancial stability and ﬁnancial intermediary ﬁrms’ behavior (2010), and Financial Intermediation Research
Society (FIRS) annual conference (2011) for helpful comments.
†
Olin Business School, Washington University in St. Louis. Email: gopalan@wustl.edu.
‡
Smeal College of Business, Pennsylvania State University. Email: song@psu.edu.
§
C.T. Bauer College of Business, University of Houston. Email: vyerramilli@bauer.uh.edu.
1 Introduction
The collapse of ﬁnancial institutions such as Bear Stearns and Lehman Brothers during the
recent ﬁnancial crisis has once again focussed attention on the risks arising from short-term
debt. It is now universally acknowledged that the proximate cause for the failure of the
two institutions was their over-reliance on short-term debt which they were unable to roll
over due to a fall in collateral values (Brunnermeier (2009)).1 The theoretical literature
has long recognized this “rollover” risk arising from short-term debt.2 Diamond (1991) and
Titman (1992) show that in the presence of credit market frictions, ﬁrms may face diﬃculty
in rolling over short-term debt, especially if reﬁnancing coincides with a deterioration in
either ﬁrm fundamentals or credit market conditions. Recent theoretical literature argues
that rollover risk may itself be an additional source of credit risk, because short-term debt
increases the possibility of a run on the ﬁrm (He and Xiong (2011a) and Morris and Shin
(2009)), and exacerbates the conﬂict of interest between shareholders and debtholders (He
and Xiong (2011b)).
Are the collapses of Bear Stearns and Lehman Brothers isolated incidents that occurred
during periods of unprecedented stress in credit markets, or is there a systematic causal
relationship between a ﬁrm’s reliance on short-term debt and subsequent deterioration in its
credit quality? Despite a large body of theoretical literature which argues that the answer is
yes, surprisingly there is no empirical paper that directly addresses this question. Identifying
such a causal link is challenging because a ﬁrm’s debt maturity structure is itself endogenous.
For example, in our sample of Compustat ﬁrms that have long-term credit ratings from S&P,
we ﬁnd that ﬁrms with a larger proportion of short-term debt are actually less risky, based on
observable risk characteristics such as size, leverage, credit rating, proﬁtability, idiosyncratic
volatility and industry volatility. Thus, without adequate controls for the endogeneity of the
debt maturity structure, one might conclude that a larger proportion of short-term debt is
1
Such risks are certainly not conﬁned to ﬁnancial ﬁrms alone, as there is a long history of high-proﬁle
bankruptcies involving non-ﬁnancial ﬁrms, where the inability to roll over short-term debt compounded the
eﬀect of operating losses and led to sudden collapses (e.g., WorldCom, Enron, First Executive Corporation,
and Penn Central).
2
Other terms employed in the literature are liquidity risk, maturity risk, and reﬁnancing risk.
1
associated with better credit outcomes. In this paper, we use a variety of empirical techniques
to overcome the endogeneity problem and identify the causal relationship between a ﬁrm’s
reliance on short-term debt and subsequent deterioration in its credit quality.
Our sample spans the time period 1986–2010, and includes all ﬁrms that have a long-term
credit rating from Standard and Poor’s (S&P) and for which ﬁnancial information is available
in the Compustat database. We measure a ﬁrm’s reliance on short-term debt or its exposure
to rollover risk using the variable Rollover, which we deﬁne as the proportion of the ﬁrm’s total
debt that is maturing within the year (henceforth, referred to as short-term debt).3 A ﬁrm’s
operating risk characteristics may jointly determine both its reliance on short-term debt (see,
for example, Barclay and Smith (1995), Stohs and Mauer (1996), and Titman and Wessels
(1988)) and the subsequent change in its credit quality. In our baseline empirical analysis,
we explicitly control for all observable ﬁrm characteristics that may aﬀect the proportion of
short-term debt, and that may also aﬀect the ﬁrm’s credit quality. We also include ﬁrm
ﬁxed eﬀects to control for time-invariant risk characteristics, rating ﬁxed eﬀects to control for
existing credit quality, and year ﬁxed eﬀects to control for systematic risk factors that may
aﬀect subsequent fall in credit quality.
We begin our analysis by examining whether ﬁrms with a larger proportion of short-term
debt are more likely to experience a fall in credit quality in the following year. Our ﬁrst
measure of credit quality deterioration is the number of notches by which a ﬁrm’s credit
rating is downgraded during the year. Our second measure is whether the ﬁrm is downgraded
to ‘D’ rating during the year. S&P assigns this rating to ﬁrms that are either in default
or are expected to default on their debt obligations. Regardless of the measure used, we
ﬁnd that once we employ our extensive empirical speciﬁcation with ﬁrm and rating ﬁxed
eﬀects, ﬁrms with a larger proportion of short-term debt (higher Rollover ) are more likely to
experience a severe deterioration in their credit quality in the following year. This eﬀect is
also economically large: a one standard deviation increase in Rollover, which represents an
increase in the proportion of short-term debt from the sample average of 15.9% to 37.2%, is
associated with a 11.7% increase in the number of notches of rating downgrade, and a 66%
3
Our results are robust to an alternative measure of exposure to rollover risk deﬁned as the proportion of
short-term debt to total assets.
2
increase in the likelihood of being downgraded to ‘D’ rating during the following year. While
an average ﬁrm in our sample has a 1.2% chance of being downgraded to ‘D’ in any year, a one
standard deviation increase in Rollover is associated with a 0.79% increase in this likelihood.
To better understand the relationship between Rollover and subsequent deterioration in
credit quality, we perform a number of cross-sectional tests. When we diﬀerentiate between
small and large ﬁrms, we ﬁnd that the positive association between Rollover and subse-
quent deterioration in credit quality is present among both small and large ﬁrms with similar
economic magnitudes. A 21.3% increase in Rollover (one standard deviation increase) is as-
sociated with a 0.83% (0.75%) increase in the likelihood of a downgrade to ‘D’ rating for
large (small) ﬁrms. When we diﬀerentiate ﬁrms based on prior credit quality, we ﬁnd that
the positive association between Rollover and subsequent deterioration in credit quality is
present only among ﬁrms with speculative grade credit rating (S&P rating below ‘BBB-’).
Moreover, consistent with theoretical predictions, we also ﬁnd that the positive association
between Rollover and subsequent deterioration in credit quality is stronger for ﬁrms that
experience a year-on-year decline in operating proﬁtability and during periods of economic re-
cession. Interestingly, the positive association between Rollover and subsequent deterioration
in credit quality is present during expansions as well.
Despite the rich empirical speciﬁcation we employ, it is still possible that some unobserved
time-varying risk characteristic can bias our estimates. This can happen if changes in the risk
characteristic aﬀects both the reliance on short-term debt as well as the subsequent change in
credit rating. As we mentioned, based on observable risk characteristics, ﬁrms in our sample
with higher values of Rollover are actually less risky. Presumably such ﬁrms are the ones with
access to the commercial paper market. To this extent, if anything, we expect unobserved
time-varying risk characteristic to have a downward bias on our OLS estimates. To identify
the magnitude of this bias, we perform several tests. We highlight two of these tests here.
First, following Almeida et al. (2009), we use the ratio of long-term debt due within the
year over total debt as an alternative measure of the ﬁrm’s exposure to rollover risk. That is,
we exclude from our measure any short-maturity debt that the ﬁrm may have issued in the
previous year that is due in the current year. Since the amount of long-term debt due within
3
the year depends on the ﬁrm’s long-term debt structure and its repayment schedule, both
of which are likely to have been determined in the past, any omitted time-varying variable
should not aﬀect this alternative measure. When we repeat our tests with this alternative
measure, we continue to ﬁnd a positive and signiﬁcant association between a ﬁrm’s exposure
to rollover risk and the severity of rating downgrades. Consistent with our OLS estimates
being downward biased, we ﬁnd that a 21.3% increase in the proportion of long-term debt due
within a year (corresponding to a one standard deviation increase in Rollover ) is associated
with a 1.02% increase in the annual likelihood of ‘D’ rating.
Second, we perform an instrumental variables (IV) estimation, where we instrument for
Rollover using the fraction of current assets in the ﬁrm’s total assets and the yield on the
10-year treasury bond. The use of the ﬁrm’s asset structure as an instrument for its debt
maturity structure is motivated by the idea that ﬁrms with more short-term assets tend to
rely more on short-term borrowing, either to avoid mismatch between assets and liabilities
or because they have fewer long-term assets that can be pledged as collateral for long-term
borrowing. Consistent with this assumption we ﬁnd that the proportion of current assets is
strongly positively associated with the proportion of short-term debt. At the same time, the
proportion of current assets on the ﬁrm’s balance sheet should not directly aﬀect subsequent
deterioration in credit quality. The identifying assumption behind using the 10-year treasury
yield as an instrument is that ﬁrms are more likely to issue short-term debt when long-term
interest rates are high (based on the market-timing argument in, among others, Baker et al.
(2003), Barclay and Smith (1995), and Guedes and Opler (1996)), but that high long-term
interest rates do not directly lead to deterioration in credit quality.
We ﬁnd that our results are robust to instrumenting for Rollover. The F-statistic of
excluded instruments in the ﬁrst stage is 52.42, which indicates that our instruments are
strong. Moreover, consistent with the OLS estimates being downward biased, we ﬁnd that
the IV coeﬃcient estimates are signiﬁcantly larger than the corresponding OLS estimates.
Based on the IV estimates, a one standard deviation increase in Rollover is associated with a
1.6% increase in the likelihood of being downgraded to a ‘D’ rating in the following year.
Long-term creditors will be adversely aﬀected by the rollover risk of short-term debt, since
4
short-term lenders get paid ﬁrst. If long-term creditors recognize this risk, then ﬁrms with
a larger proportion of short-term debt should ceteris paribus face a higher cost of long-term
borrowing. In our ﬁnal set of tests, we examine whether the yield spreads on a ﬁrm’s long-term
bonds are aﬀected by the proportion of short-term debt on the ﬁrm’s balance sheet. To do
this, we replicate the bond yield spread model in Campbell and Taksler (2003) after adding
the lagged value of Rollover as an additional regressor. We ﬁnd that bonds issued by ﬁrms
with higher values of Rollover have higher yield spreads. While our results are statistically
signiﬁcant, the economic magnitude appears small, especially in comparison to the eﬀect of
Rollover on default likelihood. We ﬁnd that all else equal, a one standard deviation increase
in Rollover is associated with a 4.5 basis point increase in the bond’s yield spread.
Our paper contributes to both the literature on debt structure and the literature on credit
risk by providing empirical validation to the theoretical predictions that reliance on short-term
debt exposes a ﬁrm to rollover risk and increases its overall credit risk (e.g., He and Xiong
(2011a), He and Xiong (2011b), and Morris and Shin (2009)). This is an important ﬁnding
because it has practical implications for a ﬁrm’s choice of its debt maturity structure. While
theoretical literature identiﬁes rollover risk as an important determinant of debt maturity
choice (e.g., Diamond (1991), and Flannery (1986)), the empirical literature on debt maturity
(e.g., Barclay and Smith (1995), Berger et al. (2005), Guedes and Opler (1996), and Stohs
and Mauer (1996)) has largely sidestepped this issue: the focus of that strand of literature is
on documenting the observable ﬁrm characteristics that can explain the ﬁrm’s debt maturity
choice.
Our paper also complements several recent studies that exploit the subprime crisis of 2007-
09 to highlight the adverse real impact to ﬁrms of not being to roll over their maturing debt.
Almeida et al. (2009) show that ﬁrms with a large proportion of their long-term debt maturing
right after August 2007 (when the subprime crisis unfolded) experienced large drops in their
real investment rates. Duchin et al. (2010) ﬁnd that the decline in corporate investment
following the subprime crisis was more pronounced among ﬁrms that had more net short-
term debt. Our paper diﬀers from these papers in two important respects. First, while these
papers examine the eﬀect of debt maturity structure on ﬁrm investments, we examine the
5
eﬀect of debt maturity structure on credit risk. Our main conclusion is that rollover risk is an
additional source of credit risk that needs to be recognized by rating agencies and bond market
investors ex ante. Second, our sample period is not conﬁned to just the crisis period and our
results show that rollover risk contributes to credit risk even during benign credit market
conditions. Our results do support the notion that rollover risk becomes more important
during recessions when credit markets are likely to be stressed.
The paper proceeds as follows. We discuss the theoretical literature and outline our key
hypotheses in Section 2. We provide a description of data and summary statistics in Section
3, and present the empirical results in Sections 4, 5 and 6. Section 7 concludes the paper.
2 Theory and Hypotheses
In this section we outline the theoretical literature and draw the two hypothesis that we test
in the subsequent section.
In an early study of optimal debt maturity structure, Diamond (1991) highlights that
short-term borrowing may subject a ﬁrm to excessive liquidation when the ﬁrm attempts to
reﬁnance by rolling over its maturing debt, especially if the reﬁnancing coincides with the
release of bad news about the ﬁrm’s prospects.4 In a more recent study, Morris and Shin
(2009) argue that, similar to bank deposits, short-term debt is prone to runs due to lack of
coordination among creditors, which can undermine the ﬁrm’s credit quality and its ability
to service its long-term creditors. They further argue that a proper measure of a ﬁrm’s credit
risk should incorporate “the probability of a default due to a run on its short-term debt when
the ﬁrm would otherwise have been solvent” (also see He and Xiong (2011a)). He and Xiong
(2011b) show that short-term debt increases the likelihood of bankruptcy by exacerbating the
conﬂict of interest between equity and debt holders (akin to the classic debt overhang problem
coined by Myers (1977)). The idea is that maturing (short-term) debt holders get paid in full
ﬁrst whenever a ﬁrm experiences rollover losses (e.g., due to an overly high interest rate upon
4
Froot et al. (1993), Sharpe (1991), and Titman (1992) show that, in the presence of credit market imper-
fections, short-term debt can lower ﬁrm value if it has to be reﬁnanced at an overly high interest rate.
6
reﬁnancing) when replacing its maturing debt with new (short-term) debt, whereas equity
holders ultimately bear such losses. Recognizing this, equity holders will choose to default
earlier at a higher fundamental ﬁrm value that the ﬁrm would otherwise have survived in the
absence of rollover risk arising from short-term debt. Acharya et al. (2011) argue that when
the current owners of assets and future buyers are all short of capital, high reﬁnance frequency
associated with short-term debt can lead to a market freeze and precipitate defaults.
One basic takeaway from all these theoretical papers is that the proportion of a ﬁrm’s debt
maturing in the short term (henceforth, referred to as short-term debt) can aﬀect the ﬁrm’s
credit quality, aside from the ﬁrm’s operating risk and leverage ratio. We refer to this as the
rollover risk hypothesis, and test two of its key predictions.
First, ﬁrms with a larger proportion of short-term debt should, all else equal, be more
likely to experience a fall in credit quality. The need to frequently roll over a large amount of
short-term debt will increase the ﬁrm’s exposure to negative operating shocks and possible de-
teriorations in credit market conditions. We use severity of rating downgrades and downgrade
to ‘D’ rating as proxies to identify a fall in credit quality. Speciﬁcally, our ﬁrst hypothesis is:
Hypothesis 1: Firms with a larger proportion of short-term debt are ceteris paribus more
likely to experience severe credit rating downgrades and are more likely to get their ratings
downgraded to ‘D’.
Second, rollover risk of short-term debt will adversely aﬀect long-term creditors, because
any rollover losses resulting from reﬁnancing of short-term debt (e.g., due to ﬁre sales of
assets under the pressure of short-term creditors) will ultimately jeopardize the ﬁrm’s ability
to repay its long-term creditors in future (Brunnermeier and Oehmke (2011) and Morris and
Shin (2009)). If long-term creditors are cognizant of the rollover risk arising from short-term
debt, then such risk should be reﬂected in the ﬁrm’s cost of long-term borrowing. Thus, our
second hypothesis is:
Hypothesis 2: Firms with a larger proportion of short-term debt should, all else equal, face
a higher cost of long-term borrowing, as manifested by a higher yield spread on their long-term
bonds.
7
3 Data and Sample Characterization
3.1 Data
We obtain data on ﬁrms’ long-term credit ratings from Standard and Poor’s (S&P); these
ratings represent S&P’s long-term assessment of a ﬁrm’s overall credit quality but not speciﬁc
to a particular security issued by the ﬁrm. This data is made available in Compustat on
a monthly basis. We transform the credit ratings into an ordinal scale ranging from 1 to
22, where 1 represents a rating of ‘AAA’ and 22 represents a rating of ‘D’ (i.e., a smaller
numerical value represents a higher rating; see the Appendix for details). We collect annual
ﬁrm ﬁnancial information from Compustat. Our sample spans the time period 1986-2010, and
consists of all ﬁrms that have an S&P long-term credit rating and are covered by Compustat.
Information on individual stock returns and returns on the CRSP value-weighted index comes
from the Center for Research in Security Prices (CRSP).
We obtain data on long-term corporate bonds from the Mergent Fixed Income Securities
Database (FISD). This database provides both issue characteristics and transaction informa-
tion for all corporate bond trades among insurance companies from the National Association
of Insurance Commissioners (NAIC) since 1995. Following Campbell and Taksler (2003), we
take the following steps to ﬁlter the FISD sample to suit our purpose. First, given that in-
surance companies often limit their investments to investment-grade assets due to regulatory
constraints, we exclude speculative-grade bonds from our sample because these trades in the
FISD database are unlikely to be representative of the general market. Next, to ease the
computation of yield to maturity for the bond, we restrict our sample to ﬁxed-rate bonds that
are not callable, puttable, convertible, substitutable, or exchangeable. To avoid dealing with
currency exchange rates, we only consider U.S. dollar-denominated bonds issued by domestic
issuers. We also drop defaulted bond issues. Finally, we exclude bonds that are asset-backed
or include any credit-enhancement features because we want the estimated yield to maturity
for the bond to be solely driven by the underlying issuer’s creditworthiness, and not by credit
enhancements that we cannot fully control for in the cross-section.
8
3.2 Key Variables
We measure a ﬁrm’s exposure to rollover risk using the variable Rollover, which is deﬁned as
the proportion of the ﬁrm’s total debt that is due within the year. Speciﬁcally, Rollover is the
ratio of total debt in current liabilities (Compustat item dlc) to the sum of debt in current
liabilities and long-term debt (sum of Compustat items dlc and dltt). Firms with higher values
of Rollover have to reﬁnance a larger proportion of their debt during the year, and hence are
likely to be exposed to greater rollover risk. As mentioned before, to address the potential
endogeneity problem, we also create an alternative measure of rollover risk, Rollover-Alt,
which is deﬁned as the ratio of long-term debt payable within a year (Compustat item dd1 )
to total debt (sum of Compustat items dlc and dltt). Note that the numerator in Rollover-Alt
excludes any short-maturity debt that the ﬁrm may have issued in the past year that is due
in the current year.
To test Hypothesis 1, we use downgrades in S&P credit rating to identify adverse changes
in a ﬁrm’s credit quality. Speciﬁcally, we employ the following measures:
1. Notches downgrade, which is deﬁned as the maximum number of notches by which a
ﬁrm’s credit rating is downgraded during any month of the year; it takes the value zero
if the ﬁrm’s rating is not downgraded during the year.
2. Multi-notch downgrade, a dummy variable that identiﬁes ﬁrms whose credit rating is
downgraded by more than one notch during any month of the year.
3. Default, a dummy variable that identiﬁes ﬁrms whose credit rating is downgraded to ‘D’,
during the year.5 S&P assigns the ‘D’ rating either when a ﬁrm has actually defaulted
on its obligations or if S&P believes that the ﬁrm will not be able to make such payments
during the applicable grace period.
While Default represents an extreme event of credit quality deterioration, the other measures
5
The following example illustrates how we construct those measures. Suppose a ﬁrm starts with a credit
rating of ‘AA’ in January. In March during the same year, its rating drops to ‘AA-’ (1-notch downgrade), and
in August the rating continues to drop to ‘A-’ (3-notch downgrade from March), and stays at ‘A-’ until the
end of the year. In this example, Notches downgrade = 3, Multi-notch downgrade = 1, and Default = 0.
9
capture a more general manifestation of deterioration in credit quality in the absence of
outright default.
To test Hypothesis 2, we use the yield spreads on a ﬁrm’s long-term bonds (Yield spread )
as a measure of the bond market’s perception of the ﬁrm’s credit risk. We estimate the yield to
maturity for each bond trade using its transaction price, time to maturity, coupon frequency
(usually semi-annual), and coupon rate. We then obtain the bond’s yield spread during a
month as the diﬀerence between its average yield to maturity imputed from all trades during
the month and the yield on a U.S. treasury security of comparable maturity. We obtain
benchmark treasury yields from the website of the Federal Reserve Board. We winsorize the
data on yield spreads at the 1% level on both sides to reduce the eﬀect of outliers.
3.3 Descriptive Statistics and Univariate Tests
We present the descriptive statistics for our full sample in Panel A of Table 1. Deﬁnitions of
all the variables are in the Appendix. Recall that our sample only includes Compustat ﬁrms
that have long-term credit ratings from S&P. The mean value of Log(Total assets) of 7.724
corresponds to an average book value of total assets of approximately $2.26 billion for our
sample ﬁrms. The corresponding value for the full Compustat sample during the same time
period is about $82 million. Thus, our sample of rated ﬁrms represents the subset of larger
ﬁrms in Compustat.
The mean value of Rollover is 0.159, which means that the average ﬁrm in our sample has
15.9% of its total debt maturing within one year. The median value of Rollover is signiﬁcantly
lower at 0.072, suggesting an upward skewness in the distribution of Rollover in our sample.
The median value of Total debt/Mkt. Cap of the ﬁrms in our sample is 0.299, and the median
value of Long-term debt/Total assets is 0.264. Firms in our sample have an average interest
coverage of 9.262. The median value of ﬁrm credit rating in our sample is about 10.6, which
corresponds to a rating slightly below ‘BBB-’. Consistent with this, we ﬁnd that about 46.5%
of the ﬁrms in our sample have investment-grade ratings (‘BBB-’ or above).
The average ﬁrm in our sample faces a 13% likelihood of experiencing a rating downgrade
10
during the year, and a 4.3% chance of experiencing a multi-notch downgrade at some point
during the year. The mean value of 1.577 on Notches downgrade (Conditional) indicates
that, conditional on experiencing a downgrade during the year, the ﬁrm’s credit rating is
downgraded by 1.577 notches on average. The average annual default likelihood of ﬁrms in
our sample is 1.2%.
Panel B provides a univariate comparison of the ﬁnancial characteristics of high-Rollover
and low-Rollover ﬁrms, where high-Rollover (low-Rollover ) ﬁrms are deﬁned as those with
above (below) sample median value of Rollover and are expected to face relatively higher
(lower) rollover risk. Observe that high-Rollover ﬁrms are on average signiﬁcantly larger in
size (higher Log(Total assets)), have lower leverage ratios (lower Total debt/Mkt. Cap and
Long term debt/Total assets) and higher values of Interest coverage than low-Rollover ﬁrms.
Moreover, we ﬁnd that high-Rollover ﬁrms are more proﬁtable (higher Operating income/Sales
and Taxes/Total assets), have less volatile stock returns (lower Idiosyncratic volatility), and
reside in industries with lower earnings volatility (lower Industry volatility) than low-Rollover
ﬁrms. Consistent with high-Rollover ﬁrms being less risky, we also ﬁnd their average credit
rating to be 9.345 (slightly below ‘BBB+’) as compared to 11.456 (slightly below ‘BBB-’) for
low-Rollover ﬁrms. Moreover, high-Rollover ﬁrms are on average signiﬁcantly more likely to
have an investment grade rating than low-Rollover ﬁrms (higher Investment grade). Thus,
along multiple observable dimensions, the average low-Rollover ﬁrm is signiﬁcantly riskier
than the average high-Rollover ﬁrm in our sample. If unobservable risk characteristics vary
in a similar manner, then lack of adequate controls for risk is likely to bias our estimates
downward.
Interestingly, despite the fact that they are less risky on average, we ﬁnd that high-Rollover
ﬁrms are signiﬁcantly more likely than low-Rollover ﬁrms to experience severe deterioration
in credit quality, as evidenced by the higher average values of Multi-notch downgrade, Notches
downgrade, and Default. While low-Rollover ﬁrms have a 3.8% probability of experiencing a
multi-notch downgrade during a year, high-Rollover ﬁrms have a 4.8% probability of experi-
encing such severe downgrades. The diﬀerences in the mean values of Default are even more
striking, suggesting that high-Rollover ﬁrms are almost twice as likely as low-Rollover ﬁrms to
11
experience a Default during a year. These large diﬀerences suggest a signiﬁcant relationship
between the proportion of short-term debt and the propensity to experience a severe fall in
credit quality.
In terms of other characteristics, we ﬁnd that on average high-Rollover ﬁrms have a
marginally higher market-to-book ratio (1.721 in comparison to 1.643), and invest more in
R&D as a proportion of total assets (higher R&D/Total assets) than low-Rollover ﬁrms. While
high-Rollover ﬁrms have slightly lower tangibility of assets than low-Rollover ﬁrms, there is
no signiﬁcant diﬀerence in the proportion of cash to total assets across the two subsamples.
Finally, we ﬁnd that as expected, high-Rollover ﬁrms have a signiﬁcantly larger proportion
of short-term current assets to total assets (higher Current assets/Total assets). In our IV
regressions, we exploit this fact and use Current assets/Total assets to instrument for Rollover.
In Panel C, we compare the average yield spreads of bonds issued by high-Rollover and
low-Rollover ﬁrms. Recall that we have information on yield spreads only for investment-
grade bonds traded during the time period 1995-2010. We present the comparison separately
for diﬀerent sectors (utility, industrial, and ﬁnancial ﬁrms), diﬀerent rating categories, and
diﬀerent maturity categories. The rating categories are obtained by dividing investment-
grade bonds into three subgroups: high-rated bonds (S&P rating ∈ {AAA, AA+, AA, AA-}),
medium-rated bonds (S&P rating ∈ {A+, A, A-}), and low-rated bonds (S&P rating ∈
{BBB+, BBB, BBB-}). Bonds are also classiﬁed as short-maturity bonds (maturity less than
7 years), medium-maturity bonds (maturity between 7 and 15 years), or long-maturity bonds
(maturity between 15 and 30 years). There is signiﬁcant evidence in Panel C that after con-
trolling for sector, rating and maturity, bonds issued by high-Rollover ﬁrms on average trade
at a higher yield spread as compared to bonds issued by low-Rollover ﬁrms. Of the 27 sector-
rating-maturity buckets in the panel, the average bond yield spread is signiﬁcantly higher for
high-Rollover ﬁrms as compared to low-Rollover ﬁrms in 14 of them. This preliminary anal-
ysis suggests that bond market investors treat ﬁrms with a larger proportion of short-term
debt as being riskier, and demand a higher yield on the long-term bonds of such ﬁrms.
[Insert Table 1 here]
12
4 Exposure to Rollover Risk and Deterioration in Credit
Quality
We now proceed to formal multivariate analysis where we can control for ﬁrm characteristics
that are likely to determine the choice of the ﬁrm’s debt maturity structure. We begin our
empirical analysis by testing Hypothesis 1, which predicts that ﬁrms with a larger proportion
of short-term debt should, all else equal, be more likely to experience a fall in their credit
quality.
4.1 Baseline Analysis to Test Hypothesis 1
To test Hypothesis 1, we estimate variants of the following OLS model:
yi,t = α + β × Rolloveri,t−1 + γ × Xi,t−1 + Rating FE + Firm FE + Year FE, (1)
where the dependent variable yi,t measures the deterioration of ﬁrm i’s credit quality in year t,
and is either Notches downgrade or Default. Recall that Notches downgrade is the maximum
number of notches by which a ﬁrm’s credit rating is downgraded during any month of the
year, and Default is a dummy variable that identiﬁes ﬁrms that have been downgraded to a
rating of ‘D’ during the year. The key independent variable is the lagged value of Rollover
during the previous year, Rolloveri,t−1 . We estimate regression (1) on a panel that has one
observation for each ﬁrm-year combination.
We control the regression for the following lagged ﬁrm characteristics (Xi,t−1 ) that may
aﬀect a ﬁrm’s choice of debt maturity structure as well as the likelihood of deterioration in
credit quality: size using Log(Total assets), leverage using Total debt/Mkt. cap, Interest cover-
age, proﬁtability using Operating income/Sales and Taxes/Total assets, growth opportunities
using Market to book and R&D/Total assets, operating risk using Industry volatility and Id-
iosyncratic volatility, and asset composition using Tangibility and Cash/Total assets. Details
on the deﬁnition of the variables are provided in the Appendix. In all the speciﬁcations, we
13
also include rating ﬁxed eﬀects along with ﬁrm ﬁxed eﬀects to control for unobserved hetero-
geneities across ﬁrms, and year ﬁxed eﬀects to control for any macroeconomic variables that
may aﬀect credit quality. The standard errors are robust to heteroscedasticity and are clus-
tered at the industry level, where we deﬁne industry at the level of Fama-French 48 industry
category.
The key empirical challenge we face is that some unobserved factor may aﬀect both Rollover
and subsequent deterioration in credit quality and thus bias our estimates. In our baseline
speciﬁcation, we employ ﬁrm ﬁxed eﬀects that will control for all time invariant observed
and unobserved factors. But some time varying unobserved factor may still bias our baseline
estimates. In Section 5 we describe a number of alternative tests we perform to control for
such unobserved time varying factors.
[Insert Table 2 here.]
We present the results of the panel OLS regression (1) in Panel A of Table 2. The dependent
variable in Columns (1) through (3) is Notches downgrade. The positive and signiﬁcant
coeﬃcient on Rollover in Column (1) indicates that ﬁrms with a larger proportion of short-
term debt are likely to experience more severe rating downgrades in the following year. Since
we have ﬁrm ﬁxed eﬀects in the speciﬁcation, the coeﬃcient measures the within-ﬁrm increase
in severity of downgrades when the ﬁrm has a larger proportion of short-term debt. The
coeﬃcient is also economically signiﬁcant: a one standard deviation increase in Rollover (.213)
is associated with a 0.024 increase in Notches downgrade, which represents a 11.7% increase
as compared to the sample mean of Notches downgrade of .205 (See Panel A of Table 1).
In terms of the coeﬃcient estimates on the control variables, we ﬁnd that rating downgrades
are more severe for ﬁrms that are smaller (negative coeﬃcient on Log(Total Assets)), highly
levered (positive coeﬃcient on Total debt/Mkt. Cap), less proﬁtable (negative coeﬃcient
on Operating income/Sales and Taxes/Total assets), have less cash on their balance sheet
(negative coeﬃcient on Cash/Total assets), and are from riskier industries (positive coeﬃcient
on Industry volatility).
In Column (2), we repeat the regression after replacing Rollover with two interaction terms,
14
Rollover × Small and Rollover × [1 − Small ], where Small is a dummy variable that identiﬁes
ﬁrms with below sample-median values of Log(Total assets). We do this to examine if the eﬀect
of Rollover on the severity of rating downgrades varies between small and large ﬁrms. We
ﬁnd that the coeﬃcients on both interaction terms are positive and signiﬁcant, which indicates
that a larger proportion of short-term debt is associated with severe rating downgrades for
both small and large ﬁrms. In Column (3), we repeat the regression after replacing Rollover
with the other two interaction terms, Rollover × Investment grade and Rollover × [1 −
Investment grade], where Investment grade is a dummy variable that identiﬁes ﬁrms with an
investment grade rating (S&P rating ‘BBB-’ or above). We ﬁnd that, not surprisingly, a larger
proportion of short-term debt is associated with severe rating downgrades only for ﬁrms with
below investment grade ratings.
In Columns (4) through (6), we repeat our analysis with Default as the dependent variable.
The positive and signiﬁcant coeﬃcient on Rollover in Column (4) indicates that ﬁrms with
a larger proportion of debt maturing within the year have a higher default likelihood. The
results are economically signiﬁcant: a one standard deviation increase in Rollover (0.213) is
associated with a 0.79% increase in the propensity to default, which is large compared to the
sample-mean probability of default of 1.2% (see Panel A of Table 1). When we distinguish
between small and large ﬁrms in Column (5), we ﬁnd that the eﬀect is present for both
small and large ﬁrms. However, when we distinguish between investment grade and below-
investment grade ﬁrms in Column (6), we ﬁnd that the positive association between Rollover
and Default is present only in the sample of below-investment grade ﬁrms.
Overall, the results in Panel A indicate that ﬁrms with a larger proportion of short-term
debt are likely to experience more severe deterioration in their credit quality. We obtain these
results after controlling for observable measures of ﬁrm risk including credit ratings. This
result is consistent with the rollover risk hypothesis, and highlights the eﬀect of debt maturity
structure on a ﬁrm’s overall credit risk. In unreported tests, we obtain similar results when
we estimate the regressions with Multi-notch downgrade instead of Notches downgrade as the
dependent variable. We also repeat our tests with the ratio of total debt due within the year
(Compustat item dlc) over total assets (Compustat item at, instead of over total debt) as
15
our alternative measure of rollover risk, and obtain results similar to the ones reported. Our
results are also robust to controlling for rating outlooks issued by S&P.
4.2 Further Tests to Hypothesis 1
To better understand the association between the proportion of short-term debt and fall in
credit quality, we do additional tests which are reported in Panel B of Table 2. One major
concern with our empirical speciﬁcation is that some recent unobserved change in ﬁrm risk
(that ﬁrm ﬁxed eﬀects will not control for) may lead to both an increase in the proportion
of short-term debt and a severe fall in credit quality. To test this alternative explanation,
we repeat our regression from Column (1) of Panel A after splitting Rollover t−1 into two
variables, Rollover t−2 and ∆Rollover, where Rollover t−2 is the value of Rollover two years
ago, and ∆Rollover measures the change in Rollover during the year t − 1. If a recent
change in ﬁrm risk is causing an increase in both Rollover and Notches downgrade, then
only ∆Rollover should be signiﬁcantly associated with Notches downgrade. However, we
ﬁnd that both Rollover t−2 and ∆Rollover are signiﬁcantly positively associated with Notches
downgrade, which indicates that our results in Panel A are not being driven only by recent
changes in ﬁrm risk. Note that the positive association between Rollover t−2 and Notches
downgrade is consistent with the rollover risk hypothesis because ﬁrms’ debt structure tends
to be sticky, i.e., Rollover t−2 is likely to be strongly correlated with Rollover t−1 .
Theory also predicts that rollover risk is more pronounced for ﬁrms with declining prof-
itability. We test this prediction in Column (2) of Panel B by estimating the regression after
replacing Rollover with two interaction terms, Rollover ×Decline and Rollover ×[1 − Decline],
where Decline is a dummy variable that identiﬁes ﬁrms that experience a decline in year-on-
year proﬁtability (Operating income/Sales). Consistent with theory, we ﬁnd that Rollover
is associated with more severe rating downgrades only for ﬁrms that experience a decline in
proﬁtability. In Column (3), we examine if economic conditions aﬀect the relation between
Rollover and severity of rating downgrades. To do this, we estimate our regression by replac-
ing Rollover with two interaction terms, Rollover ×Recession and Rollover ×[1−Recession],
where Recession identiﬁes the years classiﬁed by the NBER as recessionary. We ﬁnd that
16
while Rollover is positively associated with severe rating downgrades both during recessions
and expansions, the magnitude of the eﬀect is greater during recessions. Since credit market
conditions are likely to be related to economic conditions, this result highlights that rollover
risk is important both during periods of benign and stressed credit market conditions.
In Columns (4) through (6), we repeat our analysis in Columns (1) through (3) after re-
placing Notches downgrade with Default as the dependent variable. As can be seen, the results
are qualitatively similar. From Column (4), we ﬁnd that both Rollover t−2 and ∆Rollover are
signiﬁcantly positively associated with Default, which indicates that our results in Panel A
are not being driven only by recent changes in ﬁrm risk. The positive association between
Rollover and Default is present both in ﬁrms that experience a decline in proﬁtability and
those that do not, but the eﬀect is stronger in the former category. Similarly, the positive
association between Rollover and Default is present both in recessionary and non-recessionary
years, but the eﬀect is stronger in recession years.
5 Addressing Alternative Explanations
The important identiﬁcation challenge we face is the that the proportion of short-term debt
in a ﬁrm’s debt structure is endogenous. This has been theoretically argued and empirically
documented. Extant empirical research documents that small ﬁrms, ﬁrms with more growth
opportunities, riskier ﬁrms, and ﬁrms with larger information asymmetry rely more on short-
term debt (e.g., Barclay and Smith (1995), Stohs and Mauer (1996), and Titman and Wessels
(1988)).6 In our empirical analysis in Section 4, we explicitly control for all observable ﬁrm
characteristics that have been shown to aﬀect the proportion of short-term debt, and that
may also aﬀect the ﬁrm’s credit quality. We also include rating ﬁxed eﬀects to control for
credit quality, ﬁrm ﬁxed eﬀects to control for all time-invariant risk characteristics, and year
ﬁxed eﬀects to control for systematic risk factors.
6
Examining new bond issues, Guedes and Opler (1996) come to a somewhat diﬀerent conclusion from
Barclay and Smith (1995) and Stohs and Mauer (1996). They ﬁnd that large ﬁrms with investment-grade
credit ratings typically borrow both at the short and long ends of the maturity spectrum, whereas ﬁrms with
speculative-grade credit ratings typically borrow in the middle of the maturity spectrum.
17
Despite the rich empirical speciﬁcation we employ, some unobserved time-varying risk
factor may aﬀect both the increase in the proportion of short-term debt and the deterioration
in credit quality. In this regard, our ﬁnding that even lagged values of Rollover two years ago
are signiﬁcantly positively associated with Notches downgrade and Default (Columns (1) and
(4) of Panel B in Table 2) provide some comfort that the positive association between Rollover
and fall in credit quality is not a result of recent changes in ﬁrm risk alone. In this section,
we perform three additional sets of tests to address the identiﬁcation problem. Results are
reported in Table 3.
[Insert Table 3 here]
5.1 Rollover Risk from Reﬁnancing of Long-term Debt
Our ﬁrst set of tests are based on the idea that ﬁrms are exposed to rollover risk whenever
they reﬁnance debt, regardless of whether the debt was issued recently or in the distant past.
Thus, ﬁrms that need to reﬁnance a signiﬁcant amount of long-term debt (i.e., ﬁrms with
a high value of Rollover-Alt.) also face high rollover risk. However, since the amount of
long-term debt due within the year depends on the ﬁrm’s long-term debt structure and its
repayment schedule, both of which are likely to have been determined in the past, any omitted
variable that is not captured by ﬁrm ﬁxed eﬀects cannot cause a positive association between
Rollover-Alt and severity of rating downgrades. This idea is similar to the one employed by
Almeida et al. (2009).
In Panel A of Table 3, we repeat all the regressions in Panel A of Table 2 after replacing
Rollover with Rollover-Alt. The positive and signiﬁcant coeﬃcient on Rollover-Alt in Column
(1) shows that ﬁrms with greater proportion of long-term debt due within a year are also
likely to experience more severe deterioration in credit quality. Consistent with our OLS
estimates being biased downward, we ﬁnd that the economic magnitude of the eﬀect is greater
with Rollover-Alt. The coeﬃcient on Rollover-Alt of 0.132 is larger than the coeﬃcient on
Rollover of 0.112 (See Column (1) in Panel A of Table 2). Our estimates indicate that a one
standard deviation increase in Rollover-Alt (.127) is associated with a .017 increase in Notches
18
downgrade, which represents an increase of 8.2% over the sample mean of Notches downgrade
(.205).
In Column (2), we replace Rollover-Alt with two interaction terms, Rollover-Alt×Small
and Rollover-Alt×[1−Small ], to diﬀerentiate between small and large ﬁrms. While the coef-
ﬁcients on both interaction terms are positive, they are not signiﬁcant at conventional levels.
When we diﬀerentiate between investment grade and speculative grade ﬁrms in Column (3),
we ﬁnd that the positive association between Rollover-Alt and Notches downgrade is conﬁned
to speculative grade ﬁrms only.
In Columns (4) through (6), we repeat the regressions in Columns (1) through (3) with
Default as the dependent variable. As can be seen, our results are qualitatively similar. There
is a positive relationship between Rollover-Alt and Default that is economically large. Here
again the coeﬃcient on Rollover-Alt of 0.048 is larger than the coeﬃcient on Rollover of 0.037
(See Column (4) in Panel A of Table 2). Our estimates indicate that a one standard deviation
increase in Rollover-Alt is associated with a 0.61% increase in annual default likelihood. The
positive association between Rollover-Alt and Default is present for both small and large
ﬁrms (Column (5)), but is only conﬁned to speculative grade ﬁrms (Column (6)). Overall,
the evidence is consistent with the rollover risk hypothesis.
5.2 Instrumental Variables Regression
Our second set of tests use an instrumental variables (IV) regression approach. The ideal
instruments should aﬀect Rollover but must not have a direct eﬀect on changes in ﬁrm’s
credit quality. We use the proportion of current assets in total assets (Current assets/Total
assets) and the yield to maturity on 10-year treasury bonds (Ten year ) as instruments for
Rollover. The use of Current assets/Total assets as an instrument is motivated by the idea
that ﬁrms with more short-term assets tend to rely more on short-term borrowing. They
do this either to avoid mismatch between assets and liabilities or because they have fewer
long-term assets that can be oﬀered as collateral for long-term loans. Our univariate analysis
shows that high-Rollover ﬁrms do have a larger proportion of current assets in total assets
19
(see Panel B of Table 1). At the same time, the proportion of short-term current assets on
the balance sheet should not directly aﬀect changes in ﬁrm’s credit risk. The identifying
assumption behind using 10-year treasury rate (Ten year ) as an instrument is that ﬁrms are
more likely to issue short-term debt when long-term interest rates are high (based on the
market timing argument of Baker et al. (2003), Barclay and Smith (1995), and Guedes and
Opler (1996)), but that high long-term interest rates do not directly lead to deterioration in
credit quality.
We present the results of the IV regression implemented using the two-stage least squares
(2SLS) estimator in Panel B of Table 3. In order to ensure that the IV estimation converges, we
make two important changes to the empirical speciﬁcation. First, instead of ﬁrm ﬁxed eﬀects,
we include industry ﬁxed eﬀects at the level of the Fama-French 48 industry category. Second,
instead of the rating category ﬁxed eﬀects, we include a dummy variable, Investment grade,
that identiﬁes ﬁrms with an investment grade rating. The results of the ﬁrst-stage regression
with Rollover as the dependent variable are in Column (1). The positive and signiﬁcant
coeﬃcient on Current assets/Total assets indicates that ﬁrms with a larger proportion of
short-term assets rely more on short-term debt. On the other hand, the coeﬃcient on Ten
year is insigniﬁcant. Our instruments are strong, as seen from the F -statistic for the excluded
instruments in the ﬁrst stage. From the coeﬃcient on the other variables, we ﬁnd that ﬁrms
with a larger proportion of short-term debt are large (positive coeﬃcient on Log(Total assets)),
have higher leverage (positive coeﬃcient on Total debt/Mkt. Cap), have a higher interest
coverage (positive coeﬃcient on Interest coverage), are more likely to have investment grade
rating (positive coeﬃcient on Investment grade), invest more in research and development
(positive coeﬃcient on R&D/Total assets), and have a lower proportion of cash (negative
coeﬃcient on Cash/Total assets).
In Column (2), we present the results of the second-stage regression with Notches down-
grade as the dependent variable and the instrumented value of Rollover as the main regressor.
As can be seen, the coeﬃcient on Rollover is positive and signiﬁcant. Moreover, the coeﬃcient
of 0.397 is more than three times as large as the OLS coeﬃcient of 0.112 (see Column 1 in Panel
A of Table 2). This is consistent with our argument that the OLS regression underestimates
20
the true eﬀect of Rollover on subsequent deterioration in credit quality.
In Column (3), we present the results of the second-stage regression with Default as the
dependent variable. Note that the ﬁrst stage for this is similar to the one reported in Column
(1). Our results again show that ﬁrms with a larger proportion of short-term debt are more
likely to default during the year. Here again, we ﬁnd that the coeﬃcient estimate of 0.077
from the IV estimation is much larger than the OLS estimate of 0.037.
Overall, the results in Panel B indicate that the positive association between Rollover and
deterioration in credit quality is not being driven by unobserved time-varying risk factors.
5.3 Operating Risk versus Rollover Risk
It is possible that the ﬁrm’s operating risk jointly determines both the ﬁrm’s reliance on
short-term debt (see Stohs and Mauer (1996)) and subsequent fall in credit quality. Our
third test relies on the idea that the impact of rollover risk on credit quality is asymmetric
in nature: rollover risk could lead to a fall in credit quality by exacerbating the impact of
negative shocks, but does not lead to improvements in credit quality if the ﬁrm experiences
positive shocks. Thus, the rollover risk hypothesis predicts a positive association between
Rollover and rating downgrades, but no association between Rollover and rating upgrades.
On the other hand, operating risk should make both upgrades and downgrades more likely.
Therefore, if the positive association between Rollover and rating downgrades is being driven
by operating risk, then we should ﬁnd a similar positive association between Rollover and
rating upgrades.
To distinguish between these two explanations, we estimate the panel regression (1), with
Notches upgrade as the dependent variable, where Notches upgrade is the maximum number
of notches by which a ﬁrm’s credit rating is upgraded during any month of the year. The
results of our estimation are presented in Panel C of Table 3. The empirical speciﬁcation
in each column of Panel C is exactly the same as the corresponding column in Panel A of
Table 2. As can be seen, the coeﬃcient estimate on Rollover is statistically insigniﬁcant in all
speciﬁcations, and is close to zero in magnitude. This indicates that our earlier ﬁnding of a
21
positive association between Rollover and Notches downgrade is more likely driven by rollover
risk rather than operating risk.
Our results indicate that ﬁrms with larger proportion of short-term debt are more likely
to experience a deterioration in credit quality. A natural question to ask is if credit rating
agencies take this into account when assigning the initial credit rating. In other words, ce-
teris paribus, do ﬁrms with a larger proportion of short-term debt have lower credit ratings?
Unfortunately our ability to answer this question is aﬀected by the endogenity of ﬁrm’s debt
maturity structure choice. As we mentioned, ﬁrms with a larger proportion of short-term debt
are observationally less risky. Consistent with this, in unreported tests, when we employ an
ordered logit speciﬁcation to model the ﬁrm’s credit rating, we ﬁnd that ﬁrms with a larger
proportion of short-term debt are associated with a better credit rating. In the speciﬁcation
we do not include ﬁrm ﬁxed eﬀects because of the non-linear nature of the ordered logit model
and the incidental parameters problem (Neyman and Scott (1948)). Alternatively when we
employ an OLS speciﬁcation to model credit ratings and include ﬁrm ﬁxed eﬀects, we do not
ﬁnd a signiﬁcant relationship between Rollover and credit rating. Since an OLS speciﬁcation
is inappropriate to model a ﬁrm’s credit rating, we do not take these results to be conclusive.
To address the question of whether the proportion of short-term debt aﬀects the ﬁrm’s ex
ante credit risk, we test Hypothesis 2 to see if the proportion of short-term debt aﬀects the
cost of long-term borrowing.
6 Exposure to Rollover Risk and Cost of Long-Term
Bonds
We now test Hypothesis 2 by examining whether the proportion of short-term debt on the
ﬁrm’s balance sheet aﬀects the yield spreads on a ﬁrm’s long-term bonds. We do this by
replicating the regression model in Campbell and Taksler (2003), after including the lagged
value of Rollover as an additional regressor. Speciﬁcally, we estimate the following panel
22
regression on a panel with one observation for each bond-month pair:
Yield Spreadb,τ = α + β × Rolloveri,t−1 + γ1 × Xi,t−1 + γ2 × Xb + γ3 × Xm,τ
+ Issue rating FE + Industry or Firm FE + Year FE. (2)
In equation (2), the subscripts b, i, m, τ and t indicate the bond, the ﬁrm, the market, the
month, and the year, respectively. The dependent variable Yield spreadb,τ is the yield spread
for bond (b) measured over the month (τ ).
The ﬁrm characteristics (Xi,t−1 ) that we control for are: Average excess return and Id-
iosyncratic volatility, deﬁned as the mean and standard deviation, respectively, of the ﬁrm’s
daily “excess return” (i.e., return on the ﬁrm’s stock minus the return on the CRSP value-
weighted index) over the 180 days preceding the bond trade; Mkt. Cap/ Index, deﬁned as
the ratio of the ﬁrm’s market capitalization to the market capitalization of the CRSP value-
weighted index; the ratio of total long-term debt to the book value of total assets (Long term
debt/Assets); the ratio of total debt to the sum of the market value of equity and book value
of total liabilities (Total debt/Market value); the ratio of operating income before deprecia-
tion to net sales (Operating income/Sales); and four dummy variables that identify ﬁrms with
Interest Coverage below 5, between 5 and 10, between 10 and 20, and above 20, respectively.
The bond characteristics (Xb ) that we control for are the bond’s remaining maturity in years
(Maturity), the yield oﬀered at the time of the bond’s issue (Oﬀering yield ), and the natural
logarithm of the dollar size of the issue (Log(Amount)). The market characteristics (Xm,τ )
that we control for are: Average index and Systematic volatility, deﬁned as the mean and
standard deviation, respectively, of the daily return on the CRSP value-weighted index over
the 180 days preceding the bond transaction date; and Treasury slope, deﬁned as the diﬀerence
in yield between a 10-year treasury and a 2-year treasury.
The results of our estimation are presented in Table 4. In Column (1), we estimate the
regression on all the bonds in our sample, and include issue rating, year and industry ﬁxed
eﬀects, where industry is identiﬁed at the level of the Fama-French 48 industry category. The
positive and signiﬁcant coeﬃcient on Rollover indicates that bonds issued by ﬁrms that have
23
a larger proportion of debt maturing within the year trade at higher yield spreads, even after
controlling for all the other factors that are known to aﬀect bond yields, including the bond’s
credit rating. This result highlights that reliance on short-maturity debt increases a ﬁrm’s
overall credit risk, over and above what is captured by its credit rating.
[Insert Table 4 here]
The coeﬃcients on the control variables are consistent with those in Campbell and Tak-
sler (2003). In particular, bond yield spreads are higher for ﬁrms with higher idiosyncratic
volatility and during periods of high market volatility (positive coeﬃcients on Idiosyncratic
volatility and Systematic volatility), and are lower when market returns are high (negative
coeﬃcients on Average index ). Bond yield spreads are also lower for large bond oﬀerings and
for bonds oﬀered by large ﬁrms (negative coeﬃcient on Log(Amount) and Mkt. Cap/Index ),
and are higher for longer maturity bonds (positive coeﬃcient on Maturity) and for bonds with
a higher oﬀering yield (positive coeﬃcient on Oﬀering yield ).
In Column (2), we repeat our estimation with ﬁrm ﬁxed eﬀects instead of industry ﬁxed
eﬀects, and obtain similar results. As can be seen, the magnitude of the coeﬃcient on Rollover
is not very diﬀerent from that in Column (1). While the coeﬃcients are statistically signiﬁcant,
they are not large in economic terms. The coeﬃcient estimate from Column (2) indicates that
a one standard deviation increase in Rollover (0.244 for the sample of bond issues) is associated
with a higher bond yield spread of 4.5 basis points, which represents a 3.5% increase over the
sample mean bond yield spread of 129 basis points.
In Column (3), we repeat the regression in Column (2) after replacing Rollover with the
two interaction terms, Rollover × Small and Rollover × [1−Small ]. The coeﬃcients on both
interaction terms are positive and signiﬁcant, which indicates that a larger proportion of
short-term debt is associated with higher yields on long-term bonds for both small and large
ﬁrms. In Column (4) we repeat the regression with the interaction terms, Rollover ×High
rated and Rollover × [1−High rated ], where High rated is a dummy variable that identiﬁes
bonds with credit rating above the sample median. As can be seen, the positive association
between Rollover and yield spreads on long-term bonds is conﬁned to the low rated ﬁrms.
24
Overall, the evidence in Table 4 indicates that bond market investors seek a premium for
investing in bonds issued by ﬁrms with a high proportion of debt maturing in the short term,
even after controlling for the ﬁrm’s credit rating. This result suggests that debt maturity
structure matters independent of the credit rating. All else equal, greater reliance on short-
term debt increases the ﬁrm’s overall credit risk, but this is not captured by the ﬁrm’s credit
rating.
7 Conclusion
The collapse of ﬁnancial institutions such as Bear Stearns and Lehman Brothers during the
recent ﬁnancial crisis focussed attention on the risks arising from short-term debt. In this
paper, we examine whether a ﬁrm’s debt maturity structure aﬀects its overall credit risk.
Our analysis is also motivated by a large body of theoretical research which argues that, in
the presence of credit market imperfections, short-term debt exposes a ﬁrm to rollover risk
of not being able to reﬁnance its maturing debt, especially if reﬁnancing coincides with a
deterioration in either ﬁrm fundamentals or credit market conditions. Recent theories argue
that rollover risk is an additional source of credit risk, which we refer to as the rollover risk
hypothesis.
Our empirical ﬁndings oﬀer strong support to the rollover risk hypothesis. We ﬁnd that
ﬁrms that have a larger proportion of their debt maturing within the year are ceteris paribus
more likely to experience a severe fall in their credit quality in the following year, as measured
by downgrades in their credit ratings and the propensity to default. This eﬀect is stronger
for ﬁrms with declining proﬁtability and during recession years. Our results are robust to
instrumenting for the proportion of short-maturity debt and alternative measures of a ﬁrm’s
exposure to rollover risk. Bond market investors seem to recognize the eﬀect of rollover risk
because long-term bonds issued by ﬁrms that have a larger proportion of short-term debt
trade at higher yield spreads, all else equal.
An interesting avenue for future research is to explore whether credit rating agencies ade-
quately account for the eﬀect of rollover risk on credit risk. Our results seem to suggest that
25
they do not, because we obtain our results even after controlling for ﬁrms’ credit ratings. How-
ever, this requires further exploration, especially with regard to ratings of structured products
issued by ﬁnancial institutions, that were largely ﬁnanced with short-term debt and were at
the heart of the recent ﬁnancial crisis. The following quote from “S&P’s Rating Direct” issued
on May 13, 2008 seems to acknowledge some shortcomings in accounting for rollover risk and
promises to correct for it:
“Although we believe that our enhanced analytics will not have a material eﬀect on
the majority of our current ratings, individual ratings may be revised. For example,
a company with heavy debt maturities over the near term (especially considering
the current market conditions) would face more credit risk, notwithstanding benign
long-term prospects.”
26
References
Acharya, V., D. Gale, and T. Yorulmazer (2011). Rollover risk and market freezes. Journal of
Finance 66, 1175–1207.
Almeida, H., M. Campello, B. Laranjeira, and S. Weisbenner (2009). Corporate debt maturity and
the real eﬀects of the 2007 credit crisis. NBER working paper no. 14990.
Baker, M., R. Greenwood, and J. Wurgler (2003). The maturity of debt issues and predictable
variation in bond returns. Journal of Financial Economics 70, 261–291.
Barclay, M. J. and C. W. Smith (1995). The maturity structure of corporate debt. Journal of
Finance 50, 609–631.
Berger, A. N., M. Espinosa-Vega, S. Frame, and N. Miller (2005). Debt maturity, risk, and asym-
metric information. Journal of Finance 60, 2895–2923.
Brunnermeier, M. (2009). Deciphering the liquidity and credit crunch 2007-08. Journal of Economic
Perspectives 23 (1), 77–100.
Brunnermeier, M. K. and M. Oehmke (2011). The maturity rat race. Working Paper, Princeton
University.
Campbell, J. Y. and G. B. Taksler (2003). Equity volatility and corporate bond yields. Journal of
Finance 58, 2321–2349.
Diamond, D. (1991). Debt maturity structure and liquidity risk. Quarterly Journal of Economics 106,
709–38.
Duchin, R., O. Ozbas, and B. A. Sensoy (2010). Costly external ﬁnance, corporate investment, and
the subprime mortgage credit crisis. Journal of Financial Economics 97, 418–435.
Flannery, M. J. (1986). Asymmetric information and risky debt maturity choice. Journal of Fi-
nance 41, 19–37.
Froot, K., D. Scharfstein, and J. Stein (1993). Risk management: coordinating corporate investment
and ﬁnancing policies. Journal of Finance 48, 1629–1658.
Guedes, J. and T. Opler (1996). The determinants of the maturity of corporate debt issues. Journal
of Finance 51, 1809–1833.
He, Z. and W. Xiong (2011a). Dynamic debt runs. Review of Financial Studies, forthcoming.
He, Z. and W. Xiong (2011b). Rollover risk and credit risk. Journal of Finance, forthcoming.
Morris, S. and H. S. Shin (2009). Illiquidity component of credit risk. Working Paper, Princeton
University.
27
Myers, S. (1977). Determinants of corporate borrowing. Journal of Financial Economics 2, 147–175.
Neyman, J. and E. L. Scott (1948). Consistent estimates based on partially consistent observations.
Econometrica 16, 1–32.
Sharpe, S. A. (1991). Credit rationing, concessionary lending, and debt maturity. Journal of Banking
and Finance 15, 581–604.
Stohs, M. H. and D. C. Mauer (1996). The determinants of corporate debt maturity structure.
Journal of Business 69, 279–312.
Titman, S. (1992). Interest rate swaps and corporate ﬁnancing choices. Journal of Finance 47,
1503–1516.
Titman, S. and R. Wessels (1988). The determinants of capital structure choice. Journal of Fi-
nance 43, 1–20.
28
Appendix: Variable Deﬁnitions
The variables used in the empirical analysis are deﬁned as follows:
• Average excess return is the mean of daily excess returns relative to the CRSP value-weighted
index for each ﬁrm’s equity over the 180 days prior to (not including) the bond transaction
date.
• Average index is the mean of the CRSP value-weighted index returns over the 180 days prior
to (not including) the bond transaction date.
• Cash/Total assets is the ratio of book value of cash and marketable securities (Compustat
item che) to the book value of total assets (Compustat item at).
• Current assets/Total assets is the ratio of book value of current assets (Compustat item act)
to the book value of total assets (Compustat item at).
• Decline is a dummy variable that takes the value one if a ﬁrm experiences a decline in prof-
itability during the year as compared to the previous year, and zero otherwise. We measure
proﬁtability using Operating income/Sales.
• Default is a dummy variable that takes the value one for ﬁrms whose rating is downgraded to
‘D’ during the year, and zero otherwise.
• Downgrade is a dummy variable that takes the value one if the ﬁrm experiences a rating
downgrade during the year, and zero otherwise.
• High rated is a dummy variable that takes the value one if a bond’s credit rating is above the
sample median, and zero otherwise.
• Equity volatility is the standard deviation of daily excess returns relative to the CRSP value-
weighted index for each ﬁrm’s equity over the 180 days prior to (not including) the bond
transaction date.
• Idiosyncratic volatility is the standard deviation of daily excess returns relative to the CRSP
value-weighted index for each ﬁrm’s equity during a year.
• Industry volatility is the standard deviation of the operating income of all ﬁrms in the same
industry during the year. We deﬁne industry at the level of two-digit SIC code.
• Interest coverage is the ratio of operating income after depreciation (Compustat items oiadp
+ xint) to the total interest expenditure (Compustat item xint).
• Investment grade is a dummy variable that takes the value one if a ﬁrm’s credit rating is BBB-
or better, and zero otherwise.
29
• Log(Amount) is the natural logarithm of bond issue size.
• Large is a dummy variable that takes the value one for ﬁrms with book value of total assets
(Compustat item at) above the sample median, and zero otherwise.
• Log(Total assets) is the natural logarithm of the book value of total assets (Compustat item
at).
• Long term debt/Total assets is the ratio of total long-term debt (Compustat item dltt) to the
book value of total assets (Compustat item at).
• Mkt. Cap/Index is the ratio of the market value of equity to the value of CRSP value weighted
index of all stocks listed in NYSE, AMEX and NASDAQ.
• Market to book is the ratio of market value of total assets to the book value of total assets.
We calculate the market value of total assets as the sum of book value of total assets and the
market value of equity less the book value of equity.
• Maturity is the remaining years to ﬁnal maturity of the bond.
• Multi-notch downgrade is a dummy variable that takes the value one if the ﬁrm’s long-term
rating is downgraded by more than one notch during any month of the year, and zero otherwise.
• Notches downgrade indicates the maximum number of notches by which a ﬁrm’s credit rating
is downgraded during any month of the year.
• Notches downgrade (Conditional) indicates the maximum number of notches by which a ﬁrm’s
credit rating is downgraded during the year conditional on there being a downgrade. This
variable is missing for ﬁrms that do not experience a downgrade during the year.
• Oﬀering yield is the yield to maturity at the time of bond issuance.
• Operating income/Sales is the ratio of operating income after depreciation (Compustat item
oiadp) to total sales (Compustat item sale).
• R&D/Total assets is the ratio of research and development expenditure (Compustat item xrd )
to book value of total assets (Compustat item at). We replace missing values of research and
development expenditure as zero.
• Rating t−1 is an ordinal variable that indicates the S&P long-term credit rating of the ﬁrm in
the previous year. The variable is coded as follows: AAA = 1, AA+ = 2, AA = 3, AA- = 4,
A+ = 5, A = 6, A- = 7, BBB+ = 8, BBB = 9, BBB- = 10, BB+ = 11, BB = 12, BB- = 13,
B+ = 14, B = 15, B- = 16, CCC+ = 17, CCC = 18, CCC- = 19, CC = 20, C = 21, D = 22.
• Recession is a dummy variable that takes the value one for years 1981, 1982, 1990, 1991 and
2001, 2007-08, and zero otherwise.
30
• Rollover is the ratio of total debt in current liabilities (Compustat item dlc) to the sum of
debt in current liabilities and long-term debt (Compustat items dlc + dltt).
• Rollover-Alt is the ratio of total long-term debt due within one year (Compustat item dd1 ) to
the sum of debt in current liabilities and long-term debt (Compustat items dlc + dltt).
• Small is a dummy variable that takes the value one for ﬁrms with book value of total assets
(Compustat item at) below the sample median, and zero otherwise.
• Systematic volatility is the standard deviation of the CRSP value-weighted index returns over
the 180 days prior to (not including) the bond transaction date.
• Tangibility is the ratio of book value of property plant and equipment (Compustat item ppent)
to the book value of total assets (Compustat item at).
• Taxes/Total assets is the ratio of tax expenditure (Compustat item txt) to book value of total
assets (Compustat item at).
• Ten year is the 10-year treasury yield.
• Total debt/Mkt. Cap is the ratio of total debt (Compustat items dlc + dltt) to the market
value of equity.
• Treasury slope is the diﬀerence between the 10-year treasury yield and the 2-year treasury
yield.
• Yield spread is the diﬀerence between the average yield to maturity for all bond trades during
the month and the yield to maturity on a treasury with comparable maturity.
31
Table 1: Summary Statistics
Panel A: Descriptive statistics for the full sample
N Mean Median S.D.
Log(Total assets) 22131 7.724 7.602 1.513
Rollover 22131 0.159 0.072 0.213
Total debt/Mkt. Cap 21648 0.817 0.299 1.769
Long term debt/Total assets 22131 0.302 0.264 0.206
Interest coverage 22131 9.262 4.542 17.158
Rating 22131 10.401 10.636 3.905
Investment grade 22131 0.465 0 0.499
Downgrade 22131 0.13 0 0.336
Multi-notch downgrade 22131 0.043 0 0.203
Notches downgrade 22131 0.205 0 0.668
Notches downgrade (Conditional) 2870 1.577 1 1.131
Default 22131 0.012 0 0.108
Operating income/Sales 22110 0.085 0.091 0.173
Taxes/Total assets 22131 0.022 0.02 0.028
Market to book 21640 1.682 1.415 0.878
R&D/Total assets 22131 0.017 0 0.034
Industry volatility 21962 0.124 0.08 0.103
Idiosyncratic volatility 20320 0.029 0.026 0.013
Tangibility 22131 0.362 0.319 0.234
Cash/Total assets 22127 0.088 0.048 0.108
Current assets/Total assets 21144 0.377 0.366 0.195
This panel provides the descriptive statistics of our rating sample, which includes all ﬁrms with an S&P long-term credit rating
during the time period 1986-2010. Details on the deﬁnition of the variables are provided in the Appendix.
Panel B: Low-rollover ﬁrms versus High-rollover ﬁrms
High-rollover Low-rollover High − Low
Log(Total assets) 8.127 7.323 0.804∗∗∗
Total debt/Mkt. Cap 0.685 0.95 -0.265∗∗∗
Long term debt/Total assets 0.219 0.385 -0.166∗∗∗
Interest coverage 11.252 7.274 3.978∗∗∗
Rating 9.345 11.456 -2.111∗∗∗
Investment grade 0.588 0.342 0.246∗∗∗
Downgrade 0.133 0.126 0.007
Multi-notch downgrade 0.048 0.038 0.010∗∗∗
Notches downgrade 0.216 0.193 0.023∗∗∗
Default 0.015 0.008 0.007∗∗∗
Operating income/Sales 0.094 0.076 0.018∗∗∗
Taxes/Total assets 0.026 0.019 0.007∗∗∗
Market to book 1.721 1.643 0.078∗∗∗
R&D/Total assets 0.021 0.013 0.008∗∗∗
Industry volatility 0.116 0.131 -0.015∗∗∗
Idiosyncratic volatility 0.026 0.032 -0.006∗∗∗
Tangibility 0.337 0.388 -0.051∗∗∗
Cash/Total assets 0.087 0.089 -0.002
Current assets/Total assets 0.411 0.344 0.067∗∗∗
This panel compares the mean values of the variables used in our analysis across two subsamples identiﬁed based on whether
Rollover is below or above its sample median, Low-rollover and High-rollover, respectively. Asterisks denote statistical signiﬁcance
at the 1% (***), 5% (**) and 10% (*) levels.
32
Panel C: Yield spread
Utilities
High-Rollover Low-Rollover High − Low
High Rated Short maturity 79.63 79.56 0.06
High Rated Medium maturity 85.21 52.78 32.43∗∗∗
High Rated Long maturity 158.32 93.47 64.85∗∗∗
Medium Rated Short maturity 116.01 109.52 6.49∗
Medium Rated Medium maturity 130.32 118.96 11.37∗∗∗
Medium Rated Long maturity 180.59 144.46 36.12∗∗∗
Low Rated Short maturity 148.16 129.10 19.07∗∗∗
Low Rated Medium maturity 151.54 151.70 -0.16
Low Rated Long maturity 212.23 170.88 41.35∗∗∗
Industrial ﬁrms
High Rated Short maturity 68.22 70.58 -2.35
High Rated Medium maturity 71.24 74.73 -3.49
High Rated Long maturity 102.25 97.55 4.70
Medium Rated Short maturity 101.12 96.18 4.94∗∗∗
Medium Rated Medium maturity 107.70 107.56 0.14
Medium Rated Long maturity 143.77 129.01 14.76∗∗∗
Low Rated Short maturity 165.45 140.87 24.58∗∗∗
Low Rated Medium maturity 169.91 155.60 14.32∗∗∗
Low Rated Long maturity 196.77 190.28 6.49
Finance ﬁrms
High Rated Short maturity 93.33 103.04 -9.71
High Rated Medium maturity 119.90 89.74 30.16∗∗∗
High Rated Long maturity 145.98 140.41 5.58
Medium Rated Short maturity 91.53 126.65 -35.11∗∗∗
Medium Rated Medium maturity 122.91 143.41 -20.50∗∗∗
Medium Rated Long maturity 170.86 183.39 -12.54∗∗
Low Rated Short maturity 161.42 150.89 10.53∗∗
Low Rated Medium maturity 160.64 170.52 -9.88∗∗
Low Rated Long maturity 199.78 174.83 24.95∗∗∗
This panel provides the average yield spreads (in basis points) of the bonds in our sample for ﬁrms in three industries: utilities,
industrial and ﬁnancial. The data are collected from the Mergent Fixed Income Securities Database (FISD) for the time period
1995-2010. For each category, we split the sample into three subcategories depending on the rating of the bond: High-Rated
(AAA, AA+, AA, AA-), Medium-Rated (A+, A, A-) and Low-Rated (BBB+, BBB, BBB-). For each subcategory, we report
the mean yield spread of debts with short-term (maturity ≤ 7 years), Medium-Maturity (maturity ∈ (7 years, 15 years]) and
Long-Maturity (maturity ∈ (15 years, 30 years]), for subsamples of ﬁrms with proportion of short-term debt, as measured by
Rollover, above or below its sample median, High-Rollover and Low-Rollover, respectively. Asterisks denote statistical signiﬁcance
at the 1% (***), 5% (**) and 10% (*) levels.
33
Table 2: Debt Maturity Structure and Deterioration in Credit Quality
Panel A: Debt maturity structure and deterioration in credit quality
Notches downgrade Default
(1) (2) (3) (4) (5) (6)
Rollover .112 .037
(.036)∗∗∗ (.008)∗∗∗
Rollover × Small .095 .035
(.048)∗∗ (.012)∗∗∗
Rollover × [1-Small] .131 .039
(.054)∗∗ (.012)∗∗∗
Rollover × Investment grade .065 .003
(.041) (.002)
Rollover × [1 − Investment grade] .160 .071
(.072)∗∗ (.017)∗∗∗
Log(Total assets) -.080 -.082 -.079 -.004 -.004 -.003
(.020)∗∗∗ (.020)∗∗∗ (.020)∗∗∗ (.002)∗∗ (.002)∗∗ (.002)∗
Total debt/Mkt. Cap .062 .062 .061 .010 .010 .009
(.011)∗∗∗ (.011)∗∗∗ (.011)∗∗∗ (.002)∗∗∗ (.002)∗∗∗ (.002)∗∗∗
Interest coverage -.0007 -.0007 -.0007 -.00005 -.00005 -.00005
(.0004) (.0004) (.0004) (.00003)∗ (.00003) (.00003)
Operating income/Sales -.144 -.144 -.144 -.024 -.024 -.025
(.060)∗∗ (.060)∗∗ (.060)∗∗ (.010)∗∗ (.011)∗∗ (.011)∗∗
Taxes/Total assets -1.184 -1.181 -1.190 .067 .068 .063
(.374)∗∗∗ (.375)∗∗∗ (.370)∗∗∗ (.041) (.041)∗ (.041)
Market to book -.070 -.070 -.069 .0007 .0007 .0008
(.012)∗∗∗ (.012)∗∗∗ (.012)∗∗∗ (.001) (.001) (.001)
R&D/Total assets -.623 -.624 -.605 -.043 -.043 -.030
(.524) (.524) (.523) (.048) (.048) (.044)
Industry volatility .195 .196 .197 -.020 -.020 -.018
(.099)∗∗ (.098)∗∗ (.098)∗∗ (.012)∗ (.011)∗ (.011)∗
Idiosyncratic volatility 5.085 5.032 5.054 -.771 -.778 -.794
(5.782) (5.742) (5.751) (1.094) (1.086) (1.068)
Tangibility .100 .099 .100 .012 .012 .012
(.111) (.112) (.111) (.012) (.012) (.011)
Cash/Total assets -.237 -.238 -.241 .006 .006 .004
(.102)∗∗ (.102)∗∗ (.102)∗∗ (.012) (.012) (.012)
Const. 1.552 1.561 1.562 .046 .047 .053
(.232)∗∗∗ (.231)∗∗∗ (.231)∗∗∗ (.040) (.039) (.039)
Obs. 18669 18669 18669 18669 18669 18669
R2 .246 .246 .246 .67 .67 .672
This panel reports the results of a panel regression relating the proportion of short-term debt in the ﬁrm’s debt structure to a
deterioration in credit quality. Speciﬁcally, we estimate the following panel regression model:
yi,t = α + β × Rolloveri,t−1 + γ × Xi,t−1 + Rating FE + Firm FE + Year FE,
where the dependent variable y is Notches downgrade in columns (1)- (3) and Default in columns (4) and (6). Details on the
deﬁnition of the variables are provided in the Appendix. The standard errors are robust to heteroscedasticity and are clustered
at the industry level, where we deﬁne industry at the level of Fama-French 48 industry category. Asterisks denote statistical
signiﬁcance at the 1% (***), 5% (**) and 10% (*) levels.
34
Panel B: Debt maturity structure and deterioration in credit quality - additional tests
Notches downgrade Default
(1) (2) (3) (4) (5) (6)
Rollovert−2 .093 .027
(.045)∗∗ (.006)∗∗∗
∆Rollover .122 .042
(.041)∗∗∗ (.010)∗∗∗
Rollover × Decline .229 .054
(.050)∗∗∗ (.012)∗∗∗
Rollover × [1-Decline] .012 .023
(.034) (.007)∗∗∗
Rollover × Recession .163 .055
(.071)∗∗ (.014)∗∗∗
Rollover × [1-Recession] .090 .029
(.040)∗∗ (.009)∗∗∗
Log(Total assets) -.081 -.081 -.080 -.004 -.004 -.003
(.020)∗∗∗ (.020)∗∗∗ (.020)∗∗∗ (.002)∗∗ (.002)∗∗ (.002)∗∗
Total debt/Mkt. Cap .061 .061 .062 .010 .010 .010
(.011)∗∗∗ (.011)∗∗∗ (.011)∗∗∗ (.002)∗∗∗ (.002)∗∗∗ (.002)∗∗∗
Interest coverage -.0007 -.0006 -.0007 -.00003 -.00004 -.00006
(.0005) (.0004) (.0004) (.00004) (.00003) (.00003)∗
Operating income/Sales -.151 -.126 -.143 -.026 -.022 -.024
(.061)∗∗ (.058)∗∗ (.060)∗∗ (.011)∗∗ (.010)∗∗ (.010)∗∗
Taxes/Total assets -1.205 -1.082 -1.179 .066 .082 .069
(.381)∗∗∗ (.373)∗∗∗ (.376)∗∗∗ (.043) (.041)∗∗ (.041)∗
Market to book -.071 -.067 -.069 .0007 .001 .0008
(.012)∗∗∗ (.012)∗∗∗ (.012)∗∗∗ (.001) (.001) (.001)
R&D/Total assets -.682 -.615 -.620 -.036 -.042 -.041
(.525) (.522) (.521) (.050) (.049) (.047)
Industry volatility .193 .194 .195 -.020 -.020 -.020
(.100)∗ (.097)∗∗ (.097)∗∗ (.012)∗ (.012)∗ (.012)
Idiosyncratic volatility 5.022 5.017 5.092 -.741 -.781 -.769
(5.808) (5.660) (5.761) (1.113) (1.076) (1.087)
Tangibility .081 .094 .101 .012 .011 .012
(.112) (.111) (.111) (.012) (.012) (.012)
Cash/Total assets -.245 -.227 -.238 .006 .008 .006
(.107)∗∗ (.100)∗∗ (.101)∗∗ (.011) (.011) (.012)
Const. 1.582 1.544 1.551 .048 .045 .046
(.231)∗∗∗ (.230)∗∗∗ (.232)∗∗∗ (.040) (.039) (.040)
Obs. 18512 18669 18669 18512 18669 18669
R2 .247 .247 .246 .667 .671 .67
35
Table 3: Addressing Alternate Explanations
Panel A: Long-term debt payable in one year and deterioration in credit quality
Notches downgrade Default
(1) (2) (3) (4) (5) (6)
Rollover-Alt .132 .048
(.071)∗ (.011)∗∗∗
Rollover-Alt × Small .146 .052
(.091) (.016)∗∗∗
Rollover-Alt × [1-Small] .116 .044
(.088) (.015)∗∗∗
Rollover-Alt × Investment grade .033 .003
(.080) (.004)
Rollover-Alt × [1-Investment grade] .205 .082
(.119)∗ (.019)∗∗∗
Log(Total assets) -.082 -.081 -.081 -.004 -.004 -.004
(.020)∗∗∗ (.020)∗∗∗ (.020)∗∗∗ (.002)∗∗ (.002)∗∗ (.002)∗∗
Total debt/Mkt. Cap .062 .062 .061 .010 .010 .010
(.011)∗∗∗ (.011)∗∗∗ (.011)∗∗∗ (.002)∗∗∗ (.002)∗∗∗ (.002)∗∗∗
Interest coverage -.0006 -.0006 -.0006 -.00003 -.00003 -.00003
(.0004) (.0004) (.0004) (.00003) (.00003) (.00003)
Operating income/Sales -.146 -.145 -.145 -.025 -.025 -.025
(.061)∗∗ (.061)∗∗ (.061)∗∗ (.011)∗∗ (.011)∗∗ (.011)∗∗
Taxes/Total assets -1.173 -1.175 -1.179 .071 .070 .068
(.376)∗∗∗ (.376)∗∗∗ (.373)∗∗∗ (.041)∗ (.041)∗ (.041)∗
Market to book -.070 -.070 -.069 .0007 .0007 .0008
(.012)∗∗∗ (.012)∗∗∗ (.012)∗∗∗ (.001) (.001) (.001)
R&D/Total assets -.599 -.600 -.590 -.036 -.036 -.032
(.512) (.513) (.507) (.042) (.041) (.039)
Industry volatility .193 .193 .197 -.020 -.020 -.018
(.099)∗∗ (.099)∗ (.097)∗∗ (.011)∗ (.011)∗ (.011)∗
Idiosyncratic volatility 5.201 5.226 5.262 -.729 -.723 -.701
(5.887) (5.876) (5.904) (1.127) (1.121) (1.132)
Tangibility .096 .097 .096 .011 .011 .011
(.110) (.110) (.110) (.011) (.012) (.011)
Cash/Total assets -.256 -.255 -.255 -.0005 -.0003 -.0003
(.101)∗∗ (.101)∗∗ (.102)∗∗ (.012) (.012) (.012)
Const. 1.595 1.588 1.585 .059 .057 .054
(.232)∗∗∗ (.226)∗∗∗ (.234)∗∗∗ (.039) (.038) (.039)
Obs. 18669 18669 18669 18669 18669 18669
R2 .245 .245 .246 .669 .669 .671
This panel reports the results of a panel regression relating the ﬁrm’s ratio of long-term debt due within the year to total debt
to the ﬁrm’s deterioration in credit quality. Speciﬁcally, we estimate the following panel regression model:
yi,t = α + β × Rollover-Alti,t−1 + γ × Xi,t−1 + Rating FE + Firm FE + Year FE,
where the dependent variable y is Notches downgrade in columns (1)- (3) and Default in columns (4) and (6). Details on the
deﬁnition of the variables are provided in the Appendix. The standard errors are robust to heteroscedasticity and are clustered
at the industry level, where we deﬁne industry at the level of Fama-French 48 industry category. Asterisks denote statistical
signiﬁcance at the 1% (***), 5% (**) and 10% (*) levels.
36
Panel B: Instrumental variable regression
Short Notches downgrade Default
(1) (2) (3)
Current assets/Total assets .321
(.033)∗∗∗
Ten year .01
(.009)
Rollover .397 .077
(.158)∗∗ (.041)∗
Log(Total assets) .022 .022 -.004
(.004)∗∗∗ (.007)∗∗∗ (.002)∗∗
Total debt/Mkt. Cap .009 .043 .011
(.002)∗∗∗ (.008)∗∗∗ (.002)∗∗∗
Interest coverage .002 -.001 -.00005
(.0002)∗∗∗ (.0004)∗∗∗ (.0001)
Investment grade .024 .131 .005
(.009)∗∗ (.013)∗∗∗ (.002)∗∗
Operating income/Sales -.018 -.119 -.015
(.023) (.038)∗∗∗ (.008)∗
Taxes/Total assets .04 -.773 .079
(.14) (.224)∗∗∗ (.049)
Market to book .003 -.033 -.002
(.005) (.006)∗∗∗ (.001)∗
R&D/Total assets .545 -.037 -.070
(.141)∗∗∗ (.210) (.045)
Industry volatility -.06 .178 -.026
(.034)∗ (.085)∗∗ (.016)
Idiosyncratic volatility .357 8.805 .923
(.26) (.736)∗∗∗ (.234)∗∗∗
Tangibility .037 .062 .022
(.032) (.053) (.010)∗∗
Cash/Total assets -.189 -.274 .020
(.045)∗∗∗ (.048)∗∗∗ (.011)∗
F -Statistic of excluded instruments 52.42
Obs. 18553 18553 18553
R2 .113 .056 .078
This panel reports the results of a instrumental variables estimation relating the proportion of short-term debt to a deterioration
in credit quality. Speciﬁcally, we estimate the following panel regression model:
yi,t = α + β × Rolloveri,t−1 + γ × Xi,t−1 + Rating FE + Firm FE + Year FE,
after instrumenting for Rollover using the variables, Current assets/Total assets and Ten year. Column (1) provides the results of
the ﬁrst stage regression with Rollover as the dependent variables. Column (2) provides the results of the second stage regression
with Notches downgrade as the dependent variable, and column (3) provides the results of the second stage regression with Default
as the dependent variable. All variables are deﬁned in the Appendix. The standard errors are robust to heteroscedasticity and
are clustered at the industry level, where we deﬁne industry at the level of Fama-French 48 industry category. Asterisks denote
statistical signiﬁcance at the 1% (***), 5% (**) and 10% (*) levels.
37
Panel C: Debt maturity structure and improvement in credit quality
All Firms
(1) (2) (3)
Rollover -.021
(.030)
Rollover × Small -.026
(.038)
Rollover × [1-Small] -.015
(.033)
Rollover × Investment grade .015
(.018)
Rollover × [1 − Investment grade] -.057
(.052)
Log(Total assets) .055 .054 .054
(.010)∗∗∗ (.010)∗∗∗ (.010)∗∗∗
Total debt/Mkt. Cap -.002 -.002 -.002
(.009) (.009) (.009)
Interest coverage -.00009 -.00009 -.0001
(.0004) (.0004) (.0004)
Operating income/Sales .149 .149 .149
(.031)∗∗∗ (.031)∗∗∗ (.031)∗∗∗
Taxes/Total assets .883 .884 .886
(.243)∗∗∗ (.243)∗∗∗ (.242)∗∗∗
Market to book .047 .047 .047
(.011)∗∗∗ (.011)∗∗∗ (.011)∗∗∗
R&D/Total assets .132 .132 .119
(.356) (.357) (.352)
Industry volatility .069 .069 .068
(.059) (.059) (.059)
Idiosyncratic volatility -9.694 -9.710 -9.669
(3.242)∗∗∗ (3.250)∗∗∗ (3.246)∗∗∗
Tangibility .074 .073 .074
(.052) (.051) (.052)
Cash/Total assets -.093 -.094 -.091
(.066) (.066) (.065)
Const. -.888 -.885 -.895
(.154)∗∗∗ (.156)∗∗∗ (.152)∗∗∗
Obs. 18655 18655 18655
R2 .206 .206 .206
This panel reports the results of a panel data regression relating the proportion of short-term debt in the ﬁrm’s debt structure to
a likelihood of rating upgrade. Speciﬁcally, we estimate the following panel regression model:
yi,t = α + β × Rolloveri,t−1 + γ × Xi,t−1 + Rating FE + Firm FE + Year FE,
where the dependent variable y is Notches upgrade. Notches upgrade is the maximum number of notches by which a ﬁrm’s credit
rating is upgraded during any month of the year. All other variables are deﬁned in the Appendix. The standard errors are robust
to heteroscedasticity and are clustered at the industry level, where we deﬁne industry at the level of Fama-French 48 industry
category. Asterisks denote statistical signiﬁcance at the 1% (***), 5% (**) and 10% (*) levels.
38
Table 4: Debt maturity structure and bond yield spreads
All Firms - OLS
(1) (2) (3) (4)
Rollover .147 .186
(.080)∗ (.079)∗∗
Rollover × Small .199
(.084)∗∗
Rollover × Large .168
(.091)∗
Rollover × High rated .117
(.080)
Rollover × [1- High rated] .229
(.082)∗∗∗
Equity volatility 7.186 4.655 4.633 4.436
(2.171)∗∗∗ (2.928) (2.977) (2.986)
Systematic volatility 96.681 97.845 97.854 97.980
(5.732)∗∗∗ (5.695)∗∗∗ (5.697)∗∗∗ (5.681)∗∗∗
Long term debt/Total assets .014 .460 .459 .467
(.154) (.190)∗∗ (.190)∗∗ (.191)∗∗
Average index -128.515 -133.777 -133.656 -133.261
(15.168)∗∗∗ (13.459)∗∗∗ (13.422)∗∗∗ (13.475)∗∗∗
Average excess return 1.112 -3.313 -3.420 -2.905
(15.535) (10.162) (10.115) (10.245)
Mkt. Cap/Index -23.411 -68.740 -68.887 -67.908
(4.912)∗∗∗ (10.707)∗∗∗ (10.712)∗∗∗ (10.580)∗∗∗
Operating income/Sales -.064 .280 .286 .281
(.118) (.188) (.188) (.191)
Debt/Mkt. Cap .003 .037 .037 .039
(.009) (.015)∗∗ (.014)∗∗∗ (.015)∗∗∗
Treasury slope .033 .048 .048 .047
(.023) (.020)∗∗ (.020)∗∗ (.020)∗∗
Maturity .014 .014 .014 .014
(.0009)∗∗∗ (.0009)∗∗∗ (.0009)∗∗∗ (.0009)∗∗∗
Oﬀering yield .107 .088 .088 .088
(.008)∗∗∗ (.007)∗∗∗ (.007)∗∗∗ (.007)∗∗∗
Log(Amount) -.042 -.023 -.024 -.024
(.015)∗∗∗ (.012)∗∗ (.012)∗∗ (.012)∗∗
Const. -.695 -.817 -.812 -.779
(.223)∗∗∗ (.181)∗∗∗ (.183)∗∗∗ (.179)∗∗∗
Obs. 60386 61114 61114 61114
R2 .592 .657 .657 .657
Fixed eﬀects Industry and time Firm and time Firm and time Firm and time
This table reports the results of the regressions relating yield spread to the proportion of short-term debt:
Yield Spreadb,τ = α + β × Rolloveri,t−1 + γ1 × Xi,t−1 + γ2 × Xb + γ3 × Xm,τ + Rating FE + Industry or Firm FE + Year FE,
where the dependent variable Yield spread is the diﬀerence between the average yield to maturity for all bond trades during the
month and the yield to maturity on a treasury with comparable maturity. All other variables are deﬁned in the Appendix. The
data cover the period 1995-2010. The standard errors are robust to heteroscedasticity and are clustered at the industry level,
where we deﬁne industry at the level of Fama-French 48 industry category. Asterisks denote statistical signiﬁcance at the 1%
(***), 5% (**) and 10% (*) levels.
39